The descriptive statistics of the employed variables.

<p>The descriptive statistics of the employed variables.</p

Average energy intensity and energy consumption per inhabitant.

<p>Average energy intensity and energy consumption per inhabitant.</p

Panel unit root tests.

<p>Panel unit root tests.</p

The significant results of the pairwise Granger causality tests.

<p>The significant results of the pairwise Granger causality tests.</p

Results of Pedroni and Kao panel cointegration tests.

<p>Results of Pedroni and Kao panel cointegration tests.</p

Estimation of ECT in the Vector Error-Correction Model.

<p>Estimation of ECT in the Vector Error-Correction Model.</p

Description of the variables and data series.

<p>Description of the variables and data series.</p

Increase in RMSE vs. GDFM (%), # of quarters ahead (forecast).

<p>Source: authors’ own calculations.</p

RMSE of h-step ahead forecasts.

<p>Source: authors ‘own calculations.</p

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

<div><p>The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.</p></div